app.py

from fastapi import FastAPI
import pandas as pd
import uvicorn
import joblib
from sklearn.model_selection import train_test_split
from sklearn.ensemble import RandomForestClassifier
from sklearn.preprocessing import LabelEncoder
from pydantic import BaseModel
import gradio as gr
import os
import requests

app = FastAPI()

# ✅ Correct Hugging Face Dataset URL
DATASET_URL = "https://huggingface.co/datasets/SailajaS/CDART/resolve/9bfa82e31390ff6523f6b93777f745a78ecb2cd6/Sample_Case_Records__Real_Unique.csv?download=true"

# File path for saving dataset
DATASET_PATH = "dataset.csv"

# Function to download dataset
def download_dataset():
    print("📥 Downloading dataset from Hugging Face...")

    try:
        response = requests.get(DATASET_URL, timeout=10)

        if response.status_code == 200:
            with open(DATASET_PATH, "wb") as file:
                file.write(response.content)
            print("✅ Dataset downloaded successfully!")
        else:
            raise Exception(f"❌ Failed to download dataset: {response.status_code}")
    
    except requests.exceptions.RequestException as e:
        print(f"❌ Error downloading dataset: {e}")
        raise Exception("Dataset download failed.")

# ✅ Download dataset at startup
download_dataset()

# ✅ Load dataset with error handling
try:
    df = pd.read_csv(DATASET_PATH, encoding="utf-8", delimiter=",", on_bad_lines="skip")
except:
    try:
        df = pd.read_csv(DATASET_PATH, encoding="utf-8", delimiter=";", on_bad_lines="skip")
    except:
        raise Exception("❌ Unable to read CSV. Check delimiter and format.")

# ✅ Check if necessary columns exist
required_columns = ["Case Problem", "Feedback"]
for col in required_columns:
    if col not in df.columns:
        raise Exception(f"❌ Column '{col}' is missing from the dataset!")

# ✅ Convert "Case Problem" & "Feedback" to lowercase and remove spaces
df["Case Problem"] = df["Case Problem"].astype(str).str.strip().str.lower()
df["Feedback"] = df["Feedback"].astype(str).str.strip().str.lower()

# ✅ Train and save LabelEncoders for both input and output
case_problem_encoder = LabelEncoder()
feedback_encoder = LabelEncoder()

df["Case Problem Encoded"] = case_problem_encoder.fit_transform(df["Case Problem"])
df["Feedback Encoded"] = feedback_encoder.fit_transform(df["Feedback"])

# ✅ Save encoders
joblib.dump(case_problem_encoder, "case_problem_encoder.pkl")
joblib.dump(feedback_encoder, "feedback_encoder.pkl")

# ✅ Train Model
X = df[["Case Problem Encoded"]]
y = df["Feedback Encoded"]
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)
model = RandomForestClassifier(n_estimators=100, random_state=42)
model.fit(X_train, y_train)

# ✅ Save model
joblib.dump(model, "feedback_model.pkl")
print("✅ Model trained successfully!")

# ✅ API Input Model
class PredictionInput(BaseModel):
    case_problem: str

@app.post("/predict/")
async def predict_feedback(data: PredictionInput):
    """ Predicts feedback based on Case Problem """
    if model is None:
        return {"error": "Model is not trained yet."}

    # ✅ Load encoders
    case_problem_encoder = joblib.load("case_problem_encoder.pkl")
    feedback_encoder = joblib.load("feedback_encoder.pkl")

    # ✅ Convert input to lowercase and remove spaces
    case_problem_lower = data.case_problem.strip().lower()

    # ✅ Check if input exists in training data
    if case_problem_lower not in df["Case Problem"].values:
        valid_problems = list(df["Case Problem"].unique())  # Get valid options
        return {
            "error": f"Invalid case problem. Please enter a valid category.",
            "Valid Categories": valid_problems
        }

    try:
        case_problem_encoded = case_problem_encoder.transform([case_problem_lower])
        prediction = model.predict([[case_problem_encoded[0]]])
        feedback_predicted = feedback_encoder.inverse_transform(prediction)[0]
        return {"Predicted Feedback": feedback_predicted}
    except Exception as e:
        return {"error": str(e)}

# ✅ Gradio UI with Submit button
def gradio_interface(case_problem):
    if model is None:
        return "Model not trained yet."

    case_problem_encoder = joblib.load("case_problem_encoder.pkl")
    feedback_encoder = joblib.load("feedback_encoder.pkl")

    case_problem_lower = case_problem.strip().lower()

    if case_problem_lower not in df["Case Problem"].values:
        valid_problems = ", ".join(df["Case Problem"].unique())
        return f"Invalid case problem. Please enter a valid category. Options: {valid_problems}"

    try:
        case_problem_encoded = case_problem_encoder.transform([case_problem_lower])
        prediction = model.predict([[case_problem_encoded[0]]])
        feedback_predicted = feedback_encoder.inverse_transform(prediction)[0]
        return f"Predicted Feedback: {feedback_predicted}"
    except Exception as e:
        return f"Error: {str(e)}"

# ✅ Start API & Gradio with Submit button
def start_app():
    """ Start API and Gradio Interface """
    gr_interface = gr.Interface(
        fn=gradio_interface, 
        inputs=gr.Textbox(label="Enter Case Problem"),
        outputs=gr.Textbox(label="Predicted Feedback"),
        live=False,  # ✅ Submit button enabled
        allow_flagging="never",
    )
    gr_interface.launch(share=True, debug=True)  # ✅ Debugging enabled
    uvicorn.run(app, host="0.0.0.0", port=8000)

if __name__ == "__main__":
    start_app()